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31 results on '"KOSMIDIS, Thanos"'

1. ASCAPE: An open AI ecosystem to support the quality of life of cancer patients

Author

Lampropoulos, Konstantinos, Kosmidis, Thanos, Autexier, Serge, Savic, Milos, Athanatos, Manos, Kokkonidis, Miltiadis, Koutsouri, Tzortzia, Vizitiu, Anamaria, Valachis, Antonios, and Padron, Miriam Quintero

Subjects
Computer Science - Artificial Intelligence
Abstract

The latest cancer statistics indicate a decrease in cancer-related mortality. However, due to the growing and ageing population, the absolute number of people living with cancer is set to keep increasing. This paper presents ASCAPE, an open AI infrastructure that takes advantage of the recent advances in Artificial Intelligence (AI) and Machine Learning (ML) to support cancer patients quality of life (QoL). With ASCAPE health stakeholders (e.g. hospitals) can locally process their private medical data and then share the produced knowledge (ML models) through the open AI infrastructure.

Published
2023
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2. A hybrid artificial intelligence solution approach to aftercare for cancer patients

Author

Scherrer, Alexander, Zimmermann, Tobias, Riedel, Sinan, Venios, Stefanos, Koussouris, Sotiris, Plakia, Maria, Diamantopoulos, Sotiris, Athanassopoulos, Sotiris, Laras, Paris, Mousa, Fihmi, Zifrid, Robert, Tillil, Hartmut, Musisi, Isa Wasswa, Kosmidis, Thanos, Reis, Joaquim C., Moehler, Markus, Oestreicher, Gabrielle, Kalamaras, Ilias, Pantelidou, Konstantina, Votis, Konstantinos, and Vassiliou, Charalampos

Published
2023
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3. Digitally Assisted Planning and Monitoring of Supportive Recommendations in Cancer Patients

Author

Scherrer, Alexander, Zimmermann, Tobias, Riedel, Sinan, Mousa, Fihmi, Wasswa-Musisi, Isa, Zifrid, Robert, Tillil, Hartmut, Ulrich, Philip, Kosmidis, Thanos, Reis, Joaquim, Oestreicher, Gabrielle, Möhler, Markus, Kalamaras, Ilias, Votis, Konstantinos, Venios, Stefanos, Plakia, Maria, Diamantopoulos, Sotiris, Rannenberg, Kai, Editor-in-Chief, Soares Barbosa, Luís, Editorial Board Member, Goedicke, Michael, Editorial Board Member, Tatnall, Arthur, Editorial Board Member, Neuhold, Erich J., Editorial Board Member, Stiller, Burkhard, Editorial Board Member, Tröltzsch, Fredi, Editorial Board Member, Pries-Heje, Jan, Editorial Board Member, Kreps, David, Editorial Board Member, Reis, Ricardo, Editorial Board Member, Furnell, Steven, Editorial Board Member, Mercier-Laurent, Eunika, Editorial Board Member, Winckler, Marco, Editorial Board Member, Malaka, Rainer, Editorial Board Member, Maglogiannis, Ilias, editor, Iliadis, Lazaros, editor, Macintyre, John, editor, and Cortez, Paulo, editor

Published
2022
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4. SNF-CLIMEDIN: A prospective randomized trial of digital intervention in patients with advanced NSCLC—A HeCOG study.

Author

Kosmidis, Paris A., primary, Kosmidis, Thanos, additional, Papadopoulou, Kyriaki, additional, Korfiatis, Nikolaos, additional, Vozikis, Athanassios, additional, Lampaki, Sofia, additional, Psyrri, Amanda, additional, Fountzilas, Elena, additional, Christopoulou, Athina, additional, Samantas, Epaminondas, additional, Vagionas, Anastasios, additional, Mountzios, Giannis Socrates, additional, Gkoumas, Georgios, additional, Tsoukalas, Nikolaos, additional, Athanasiadis, Ilias, additional, Bafaloukos, Dimitris, additional, Panopoulos, Chris G., additional, Koufaki, Margarita Ioanna, additional, Fountzilas, George, additional, and Linardou, Helena, additional

Published
2024
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5. Implementing geriatric assessment for dose optimization of CDK 4/6 inhibitors in older breast cancer patients (IMPORTANT trial): A pragmatic randomized-controlled trial.

Author

Valachis, Antonios, primary, Biganzoli, Laura, additional, Christopoulou, Athina, additional, Fjermeros, Kamilla, additional, Fountzila, Eleni, additional, Geisler, Jürgen, additional, Gomez-Bravo, Raquel, additional, Karihtala, Peeter, additional, Kosmidis, Paris A., additional, Koutras, Angelos, additional, Linardou, Helena, additional, Lindman, Henrik, additional, Meattini, Icro, additional, Muñoz, Montserrat, additional, Psyrri, Amanda, additional, Risi, Emanuela, additional, Schiza, Aglaia, additional, Utriainen, Meri, additional, Visani, Luca, additional, and Kosmidis, Thanos, additional

Published
2024
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7. Using Association Rules in Antimicrobial Resistance in Stone Disease Patients

Author

Manolitsis, Ioannis, primary, Feretzakis, Georgios, additional, Tzelves, Lazaros, additional, Kalles, Dimitris, additional, Loupelis, Evangelos, additional, Katsimperis, Stamatis, additional, Kosmidis, Thanos, additional, Anastasiou, Athanasios, additional, Koutsouris, Dimitrios, additional, Kofopoulou, Stavroula, additional, Verykios, Vassilios S., additional, Skolarikos, Andreas, additional, and Varkarakis, Ioannis, additional

Published
2022
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8. Digital Guardian Angel Supported by an Artificial Intelligence System to Improve Quality of Life, Well-being, and Health Outcomes of Patients With Cancer (ONCORELIEF): Protocol for a Single Arm Prospective Multicenter Pilot Study

Author

Reis, Joaquim, primary, Travado, Luzia, additional, Scherrer, Alexander, additional, Kosmidis, Thanos, additional, Venios, Stefanos, additional, Laras, Paris Emmanouil, additional, Oestreicher, Gabrielle, additional, Moehler, Markus, additional, Parolini, Margherita, additional, Passardi, Alessandro, additional, Meggiolaro, Elena, additional, Martinelli, Giovanni, additional, Petracci, Elisabetta, additional, Zingaretti, Chiara, additional, Diamantopoulos, Sotiris, additional, Plakia, Maria, additional, Vassiliou, Charalampos, additional, Mousa, Suheib, additional, Zifrid, Robert, additional, Sullo, Francesco Giulio, additional, and Gallio, Chiara, additional

Published
2023
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9. The Application of Machine Learning Techniques in Prediction of Quality of Life Features for Cancer Patients

Author

Savic, Milos, Kurbalija, Vladimir, Ilic, Mihailo, Ivanovic, Mirjana, Jakovetic, Dusan, Valachis, Antonis, Autexier, Serge, Rust, Johannes, Kosmidis, Thanos, Savic, Milos, Kurbalija, Vladimir, Ilic, Mihailo, Ivanovic, Mirjana, Jakovetic, Dusan, Valachis, Antonis, Autexier, Serge, Rust, Johannes, and Kosmidis, Thanos

Abstract

Quality of life (QoL) is one of the major issues for cancer patients. With the advent of medical databases containing large amounts of relevant QoL infor-mation it becomes possible to train predictive QoL models by machine learning (ML) techniques. However, the training of predictive QoL models poses several challenges mostly due to data privacy concerns and missing values in patient data. In this paper, we analyze several classification and regression ML models predicting QoL indicators for breast and prostate cancer patients. Three different approaches are employed for imputing missing values, and several settings for data privacy pre-serving are tested. The examined ML models are trained on datasets formed from two databases containing a large number of anonymized medical records of can-cer patients from Sweden. Two learning scenarios are considered: centralized and federated learning. In the centralized learning scenario all patient data coming from different data sources is collected at a central location prior to model training. On the other hand, federated learning enables collective training of machine learning models without data sharing. The results of our experimental evaluation show that the predictive power of federated models is comparable to that of centrally trained models for short-term QoL predictions, whereas for long-term periods centralized models provide more accurate QoL predictions. Furthermore, we provide insights into the quality of data preprocessing tasks (missing value imputation and differen-tial privacy).

Published
2023
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10. Digital Guardian Angel Supported by an Artificial Intelligence System to Improve Quality of Life, Well-being, and Health Outcomes of Patients With Cancer (ONCORELIEF): Protocol for a Single Arm Prospective Multicenter Pilot Study (Preprint)

Author

Reis, Joaquim, primary, Travado, Luzia, additional, Scherrer, Alexander, additional, Kosmidis, Thanos, additional, Venios, Stefanos, additional, Laras, Paris Emmanouil, additional, Oestreicher, Gabrielle, additional, Moehler, Markus, additional, Parolini, Margherita, additional, Passardi, Alessandro, additional, Meggiolaro, Elena, additional, Martinelli, Giovanni, additional, Petracci, Elisabetta, additional, Zingaretti, Chiara, additional, Diamantopoulos, Sotiris, additional, Plakia, Maria, additional, Vassiliou, Charalampos, additional, Mousa, Suheib, additional, Zifrid, Robert, additional, Sullo, Francesco Giulio, additional, and Gallio, Chiara, additional

Published
2023
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12. Artificial intelligence supporting cancer patients across Europe - The ASCAPE project

Author

Tzelves, Lazaros, Manolitsis, Ioannis, Varkarakis, Ioannis, Ivanovic, Mirjana, Kokkonidis, Miltiadis, Useros, Cristina Sabater, Kosmidis, Thanos, Muñoz, Montserrat, Grau, Imma, Athanatos, Manos, Vizitiu, Anamaria, Lampropoulos, Konstantinos, Koutsouri, Tzortzia, Stefanatou, Dimitra, Perrakis, Konstantinos, Stratigaki, Christina, Autexier, Serge, Kosmidis, Paris, Valachis, Antonis, Tzelves, Lazaros, Manolitsis, Ioannis, Varkarakis, Ioannis, Ivanovic, Mirjana, Kokkonidis, Miltiadis, Useros, Cristina Sabater, Kosmidis, Thanos, Muñoz, Montserrat, Grau, Imma, Athanatos, Manos, Vizitiu, Anamaria, Lampropoulos, Konstantinos, Koutsouri, Tzortzia, Stefanatou, Dimitra, Perrakis, Konstantinos, Stratigaki, Christina, Autexier, Serge, Kosmidis, Paris, and Valachis, Antonis

Abstract

INTRODUCTION: Breast and prostate cancer survivors can experience impaired quality of life (QoL) in several QoL domains. The current strategy to support cancer survivors with impaired QoL is suboptimal, leading to unmet patient needs. ASCAPE aims to provide personalized- and artificial intelligence (AI)-based predictions for QoL issues in breast- and prostate cancer patients as well as to suggest potential interventions to their physicians to offer a more modern and holistic approach on cancer rehabilitation. METHODS AND ANALYSES: An AI-based platform aiming to predict QoL issues and suggest appropriate interventions to clinicians will be built based on patient data gathered through medical records, questionnaires, apps, and wearables. This platform will be prospectively evaluated through a longitudinal study where breast and prostate cancer survivors from four different study sites across the Europe will be enrolled. The evaluation of the AI-based follow-up strategy through the ASCAPE platform will be based on patients' experience, engagement, and potential improvement in QoL during the study as well as on clinicians' view on how ASCAPE platform impacts their clinical practice and doctor-patient relationship, and their experience in using the platform. ETHICS AND DISSEMINATION: ASCAPE is the first research project that will prospectively investigate an AI-based approach for an individualized follow-up strategy for patients with breast- or prostate cancer focusing on patients' QoL issues. ASCAPE represents a paradigm shift both in terms of a more individualized approach for follow-up based on QoL issues, which is an unmet need for cancer survivors, and in terms of how to use Big Data in cancer care through democratizing the knowledge and the access to AI and Big Data related innovations. TRIAL REGISTRATION: Trial Registration on clinicaltrials.gov: NCT04879563.

Published
2022
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13. Training ChatGPT Models in Assisting Urologists in Daily Practice.

Author

MANOLITSIS, Ioannis, FERETZAKIS, Georgios, TZELVES, Lazaros, KALLES, Dimitris, KATSIMPERIS, Stamatis, ANGELOPOULOS, Panagiotis, ANASTASIOU, Athanasios, KOUTSOURIS, Dimitrios, KOSMIDIS, Thanos, VERYKIOS, Vassilios S., SKOLARIKOS, Andreas, and VARKARAKIS, Ioannis

Abstract

Artificial Intelligence (AI) has shown the ability to enhance the accuracy and efficiency of physicians. ChatGPT is an AI chatbot that can interact with humans through text, over the internet. It is trained with machine learning algorithms, using large datasets. In this study, we compare the performance of using a ChatGPT API 3.5 Turbo model to a general model, in assisting urologists in obtaining accurate, valid medical information. The API was accessed through a Python script that was applied specifically for this study based on 2023 EAU guidelines in PDF format. This custom-trained model leads to providing doctors with more precise, prompt answers about specific urologic subjects, thus helping them, ultimately, providing better patient care. [ABSTRACT FROM AUTHOR]

Published
2023
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14. Acceptance of Artificial Intelligence in Supporting Cancer Patients.

Author

MANOLITSIS, Ioannis, TZELVES, Lazaros, FERETZAKIS, Georgios, KALLES, Dimitris, VERYKIOS, Vassilios S., KATSIMPERIS, Stamatis, ANASTASIOU, Athanasios, KOUTSOURIS, Dimitrios, KOSMIDIS, Thanos, DELIVELIOTIS, Charalambos, PAPATSORIS, Athanasios, and VARKARAKIS, Ioannis

Abstract

ASCAPE Project is a study aiming to implement the advances of Artificial Intelligence (AI), to support prostate cancer survivors, regarding quality of life issues. The aim of the study is to determine characteristics of patients who accepted to join ASCAPE project. It results that participants of the study mainly originate from higher-educated societies that are better informed about the potential benefits of AI in medicine. Therefore, efforts should be focused on eliminating patients’ reluctancy by better informing them on the potential benefits of AI. [ABSTRACT FROM AUTHOR]

Published
2023
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15. Artificial intelligence supporting cancer patients across Europe—The ASCAPE project

Author

Tzelves, Lazaros, primary, Manolitsis, Ioannis, additional, Varkarakis, Ioannis, additional, Ivanovic, Mirjana, additional, Kokkonidis, Miltiadis, additional, Useros, Cristina Sabater, additional, Kosmidis, Thanos, additional, Muñoz, Montserrat, additional, Grau, Imma, additional, Athanatos, Manos, additional, Vizitiu, Anamaria, additional, Lampropoulos, Konstantinos, additional, Koutsouri, Tzortzia, additional, Stefanatou, Dimitra, additional, Perrakis, Konstantinos, additional, Stratigaki, Christina, additional, Autexier, Serge, additional, Kosmidis, Paris, additional, and Valachis, Antonis, additional

Published
2022
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16. Abstract P4-11-28: Collecting quality of life information in a cohort of breast cancer survivors- Integrating electronic data collection into clinical practice

Author

Amat, Clara, primary, Fuentes, Ángeles, additional, Grau, Imma, additional, Lozano-Rubí, Raimundo, additional, Martinez-Sáez, Olga, additional, Chic, Nuria, additional, Adamo, Barbara, additional, Vidal, Maria, additional, Fernandez, Irene, additional, Valachis, Antonis, additional, Kosmidis, Thanos, additional, Kokkonidis, Miltiadis, additional, and Muñoz, Montserrat, additional

Published
2022
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19. MP50-01 ARTIFICIAL INTELLIGENCE SUPPORTING CANCER PATIENTS ACROSS EUROPE- THE ASCAPE PROJECT

Author

Tzelves, Lazaros, primary, Varkarakis, Ioannis, additional, Anastasiou, Athanasios, additional, Skolarikos, Andreas, additional, Manolitsis, Ioannis, additional, Valachis, Antonios, additional, Autexier, Serge, additional, Itu, Lucian, additional, Ivanović, Mirjana, additional, Kosmidis, Thanos, additional, Perakis, Konstantinos, additional, Rust, Johannes, additional, and Kosmidis, Paris, additional

Published
2021
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23. ASCAPE : An open AI ecosystem to support the quality of life of cancer patients

Author

Lampropoulos, Konstantinos, Kosmidis, Thanos, Autexier, Serge, Savic, Milos, Athanatos, Manos, Kokkonidis, Miltiadis, Koutsouri, Tzortzia, Vizitiu, Anamaria, Valachis, Antonis, Padron, Miriam Quintero, Lampropoulos, Konstantinos, Kosmidis, Thanos, Autexier, Serge, Savic, Milos, Athanatos, Manos, Kokkonidis, Miltiadis, Koutsouri, Tzortzia, Vizitiu, Anamaria, Valachis, Antonis, and Padron, Miriam Quintero

Abstract

The latest cancer statistics indicate a decrease in cancer-related mortality. However, due to the growing and ageing population, the absolute number of people living with cancer is set to keep increasing. This paper presents ASCAPE, an open AI infrastructure that takes advantage of the recent advances in Artificial Intelligence (AI) and Machine Learning (ML) to support cancer patients' quality of life (QoL). With ASCAPE health stakeholders (e.g. hospitals) can locally process their private medical data and then share the produced knowledge (ML models) through the open AI infrastructure.

Published
2021
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24. Abstract OT-39-01: Artificial intelligence supporting cancer patients across Europe - the ASCAPE project for breast cancer patients

Author

Valachis, Antonios, primary, Autexier, Serge, additional, Grau, Imma, additional, Itu, Lucian, additional, Jakotevic, Dusan, additional, Kosmidis, Thanos, additional, Muñoz, Montserrat, additional, Perakis, Konstantinos, additional, Rust, Johannes, additional, Savic, Milos, additional, and Kosmidis, Paris, additional

Published
2021
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25. CrowdHEALTH: An e-Health Big Data Driven Platform towards Public Health Policies

Author

Mavrogiorgou, Argyro, primary, Kiourtis, Athanasios, primary, Maglogiannis, Ilias, primary, Kyriazis, Dimosthenis, primary, De Nigro, Antonio, primary, Blanes-Selva, Vicent, primary, García-Gómez, Juan, primary, Menychtas, Andreas, primary, Sorić, Maroje, primary, Jurak, Gregor, primary, Luštrek, Mitja, primary, Gradišek, Anton, primary, Kosmidis, Thanos, primary, Nifakos, Sokratis, primary, Perakis, Konstantinos, primary, Miltiadou, Dimitrios, primary, and Gallos, Parisis, primary

Published
2020
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27. The CrowdHEALTH project and the Hollistic Health Records: Collective Wisdom Driving Public Health Policies

Author

Kyriazis, Dimosthenis, primary, Autexier, Serge, additional, Brondino, Iv, additional, Boniface, Michael, additional, Donat, Lucas, additional, Engen, Vegard, additional, Fernandez, Rafael, additional, Peris, Ricardo, additional, Jordan, Blanca, additional, Jurak, Gregor, additional, Kiourtis, Athanasios, additional, Kosmidis, Thanos, additional, Lustrek, Mitja, additional, Maglogiannis, Ilias, additional, Mantas, John, additional, Martinez, Antonio, additional, Mavrogiorgou, Argyro, additional, Menychtas, Andreas, additional, Montandon, Lydia, additional, Nechifor, Cosmin, additional, Nifakos, Sokratis, additional, Papageorgiou, Alexandra, additional, Martinez, Marta, additional, Perez, Manuel, additional, Plagianakos, Vassilis, additional, Stanimirovic, Dalibor, additional, Starc, Gregor, additional, Tomson, Tanja, additional, Torelli, Francesco, additional, Salcedo, Vicente, additional, Vassilacopoulos, George, additional, Magdalinou, Andriana, additional, and Wajid, Usman, additional

Published
2019
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29. CrowdHEALTH: Holistic Health Records and Big Data Analytics for Health Policy Making and Personalized Health.

Author

KYRIAZIS, Dimosthenis, AUTEXIER, Serge, BRONDINO, Iván, BONIFACE, Michael, DONAT, Lucas, ENGEN, Vegard, FERNANDEZ, Rafael, JIMENEZ-PERIS, Ricardo, JORDAN, Blanca, JURAK, Gregor, KIOURTIS, Athanasios, KOSMIDIS, Thanos, LUSTREK, Mitja, MAGLOGIANNIS, Ilias, MANTAS, John, MARTINEZ, Antonio, MAVROGIORGOU, Argyro, MENYCHTAS, Andreas, MONTANDON, Lydia, and NECHIFOR, Cosmin-Septimiu

Abstract

Today's rich digital information environment is characterized by the multitude of data sources providing information that has not yet reached its full potential in eHealth. The aim of the presented approach, namely CrowdHEALTH, is to introduce a new paradigm of Holistic Health Records (HHRs) that include all health determinants. HHRs are transformed into HHRs clusters capturing the clinical, social and human context of population segments and as a result collective knowledge for different factors. The proposed approach also seamlessly integrates big data technologies across the complete data path, providing of Data as a Service (DaaS) to the health ecosystem stakeholders, as well as to policy makers towards a "health in all policies" approach. Cross-domain co-creation of policies is feasible through a rich toolkit, being provided on top of the DaaS, incorporating mechanisms for causal and risk analysis, and for the compilation of predictions. [ABSTRACT FROM AUTHOR]

Published
2017
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30. Implementing geriatric assessment for dose optimization of CDK4/6 inhibitors in older breast cancer patients.

Author

Valachis A, Biganzoli L, Christopoulou A, Fjermeros K, Fountzila E, Geisler J, Gomez-Bravo R, Karihtala P, Kosmidis P, Koutras A, Linardou H, Lindman H, Martínez-Ballestero I, Rodríguez AB, Meattini I, Munoz-Mateu M, Othman M, Psyrri A, Risi E, Schiza A, Spathas N, Utriainen M, Visani L, Ballesteros S, Basdekis I, Hay SD, Fotis T, Fricker S, Graaf G, Jenset M, Kanters T, Lampropoulos K, Markou C, Mastoraki K, Nanou C, Reales Aviles JM, Santaholma M, and Kosmidis T

Abstract

Current evidence from both randomized trials and real-world studies suggests that older patients with advanced hormone receptor-positive/HER2-negative (HR+/HER2) breast cancer derive clinical benefit from the addition of CDK4/6 inhibitors to endocrine therapy. However, a higher risk for adverse events due to CDK4/6 inhibitors among older patients is evident, leading to a trend of initiating CDK4/6 inhibitors at lower dose in clinical practice, though without evidence. The aim of the IMPORTANT-trial, a pragmatic, multinational, open-label, partly decentralized randomized trial is to investigate whether lower starting dose of CDK4/6 inhibitors combined with endocrine therapy is comparable to full dose in older (≥70 years old) patients with advanced HR+/HER2- breast cancer who are assessed as vulnerable or frail based on comprehensive geriatric assessment. Clinical Trial Registration: NCT06044623 (ClinicalTrials.gov); Registration date: 13 September 2023.

Published
2024
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31. Using Association Rules in Antimicrobial Resistance in Stone Disease Patients.

Author

Manolitsis I, Feretzakis G, Tzelves L, Kalles D, Loupelis E, Katsimperis S, Kosmidis T, Anastasiou A, Koutsouris D, Kofopoulou S, Verykios VS, Skolarikos A, and Varkarakis I

Subjects
Drug Resistance, Bacterial, Humans, Anti-Bacterial Agents pharmacology, Anti-Bacterial Agents therapeutic use, Urinary Tract Infections drug therapy
Abstract

Association rule mining is a very popular unsupervised machine learning technique for discovering patterns in large datasets. Patients with stone disease commonly suffer from urinary tract infections (UTI), complicated by the emergence of antimicrobial resistance (AMR), due to the excessive use of antibiotics. In this study, we explore the use of association rule mining in the AMR profile of patients suffering from stone disease.

Published
2022
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